Implementing effective guardrails for AI agents

As artificial intelligence (AI) continues to reshape software development, AI agents are emerging as powerful tools that can work alongside development teams to automate complex tasks, generate code, and streamline development workflows. AI agents promise unprecedented efficiency gains but also introduce new security risks that organizations must consider. From autonomous code generation to automated infrastructure management, AI agents handle increasingly sensitive operations that traditionally require human oversight. The shift to AI agents raises important questions about security, compliance, and risk management in modern development environments.

Establishing robust guardrails is a business imperative for technology leaders planning to incorporate AI agents into their development processes. Leaders need to ensure that AI systems operate within defined boundaries while maintaining the agility that makes them valuable.

Why AI guardrails matter

The concept of guardrails in AI systems extends beyond traditional security controls. These guardrails are a comprehensive framework of policies, controls, and monitoring mechanisms that govern how AI agents interact with your development environment. They ensure that AI systems operate safely and effectively while complying with organizational policies and regulatory requirements. As AI agents become more embedded in DevSecOps workflows, these protective measures will be crucial for maintaining security, compliance, and operational stability.

Why are guardrails so critical when it comes to AI agents? Here are a few of the challenges we expect DevSecOps teams to encounter with the deeper integration of AI agents into their workflows:

Audit and compliance requirements: Organizations operating in regulated industries face strict requirements for tracking and justifying system changes. Our research shows that DevSecOps teams need comprehensive audit trails that capture when AI systems make changes and the human oversight involved. This dual-layer tracking is particularly crucial when AI agents and human operators work in tandem, as both the automated actions and human approvals must be documented. For regulated industries, this creates a clear chain of accountability that demonstrates who initiated changes, which AI agents were involved, and the reasoning behind each decision.

Infrastructure protection: Protecting critical infrastructure from unintended changes has emerged as a primary concern among DevOps leaders integrating AI systems. Unintended modifications to critical infrastructure components present a significant risk that must be carefully managed. Our research uncovered scenarios where automated systems could inadvertently alter crucial configurations for load balancers or databases. Organizations can prevent these potentially disruptive changes by implementing multiple review requirements and forbidden command controls while maintaining the benefits of AI automation.

License and code compliance: With the rise of AI-generated code, the challenge of managing code provenance has become increasingly complex. The security teams we interviewed emphasized the growing difficulty of maintaining clean intellectual property rights and ensuring compliance with open source licensing obligations. This is particularly crucial for organizations that must maintain strict control over their intellectual property or adhere to specific licensing requirements. Effective guardrails must include mechanisms for tracking and verifying the origin of AI-generated code while ensuring compliance with licensing obligations.

Production data security: Enterprise security leaders consistently emphasize the critical importance of maintaining existing data access controls when implementing AI systems. This is especially relevant when dealing with customer data or regulated information that requires special handling. Our research shows that granular access controls are essential for ensuring AI agents operate within established security boundaries, preventing unauthorized access to sensitive data while enabling productive automation.

Learn how agentic AI built on top of a comprehensive DevSecOps platform can help teams adopt AI agents in a way that empowers developers while preserving security, compliance, and governance.

Key guardrails for AI agents

Based on our comprehensive interviews with 54 DevSecOps practitioners and leaders — including developers, DevOps teams, SecOps, InfraOps, and CIOs — from organizations of all sizes, we’ve identified several critical types of guardrails:

User roles and access

Security begins with robust authentication and access control. Organizations should implement two-factor authentication or single sign-on (SSO) before granting AI tools access to any systems. This ensures proper user attribution and maintains security standards. Additionally, role-based access control (RBAC) is crucial for AI operations involving sensitive resources such as secrets, credentials, and protected branches.

Limits and controls

To maintain operational safety, organizations need clear boundaries around AI agent actions. This includes preventing direct production deployments without manual review and ensuring all AI-generated changes go through established merge request and review processes. Cost control measures are equally important, with manual approval requirements for actions that exceed defined thresholds. Organizations should also implement multiple review requirements for infrastructure or resource deletion and maintain robust rollback capabilities for all AI agent actions.

Customization

Every organization has unique security requirements and operational procedures. Effective guardrails must be customizable to accommodate these differences. This includes admin controls for forbidden commands (e.g., erasing Terraform state, changing domain names), configurable human touchpoints within workflows based on customer impact, and adjustable automation levels for different user roles. Integrating existing change management processes ensures AI agents work within established operational frameworks.

Logging, tracking, and transparency

Maintaining visibility into AI agent actions is crucial for security and compliance. Organizations need comprehensive SecOps logging for all AI-initiated changes, clear explanations for AI decisions (especially regarding role-based trade-offs), and robust licensing compliance checks for AI-generated and third-party code. Granular production data access controls based on compliance requirements protect sensitive information.

Learning and iterating together

Our research has revealed a crucial insight: Security measures should protect organizations without creating unnecessary friction. This ensures organizations can confidently adopt AI capabilities while maintaining robust security and compliance standards.

AI guardrails will need to adapt and grow as technology continues to evolve. Organizations implementing these protective measures today will be better positioned to leverage AI agents while maintaining security and compliance. The key is finding the right balance between enabling innovation and maintaining control — a balance that well-designed guardrails help achieve.